Digital Coding of Waveforms

Image denoising methods have been implemented in both spatial and transform domains. Each domain has its advantages and shortcomings, which can be complemented by each other. State-of-the-art methods like block-matching 3D filtering (BM3D) therefore combine both domains. However, implementation of such methods is not trivial. We offer a hybrid method that is surprisingly easy to implement and yet rivals BM3D in quality.

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Dual-domain image denoising

A novel triple-image encryption and hiding algorithm based on chaos, compressive sensing and 3D DCT

In medical imaging systems, denoising is one of the important image processing tasks. Automatic noise removal will improve the quality of diagnosis and requires careful treatment of obtained imagery. Com-puted tomography (CT) and X-Ray imaging systems use the X radiation to capture images and they are usually corrupted by noise following a Poisson distribution. Due to the importance of Poisson noise re-moval in medical imaging, there are many state-of-the-art methods that have been studied in the image processing literature. These include methods that are based on total variation (TV) regularization, wave-lets, principal component analysis, machine learning etc. In this work, we will provide a review of the following important Poisson removal methods: the method based on the modified TV model, the adaptive TV method, the adaptive non-local total variation method, the method based on the higher-order natural image prior model, the Poisson reducing bilateral filter, the PURE-LET method, and the variance stabi-lizing transform-based methods. Our task focuses on methodology overview, accuracy, execution time and their advantage/disadvantage assessments. The goal of this paper is to provide an apt choice of denoising method that suits to CT and X-ray images. The integration of several high-quality denoising methods in image processing software for medical imaging systems will be always excellent option and help further image analysis for computer-aided diagnosis.

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A Review on CT and X-Ray Images Denoising Methods

The discrete cosine transform (DCT), introduced by Ahmed, Natarajan and Rao, has been used in many applications of digital signal processing, data compression and information hiding. There are four types of the discrete cosine transform. In simulating the discrete cosine transform, we propose a generalized discrete cosine transform with three parameters, and prove its orthogonality for some new cases. A new type of discrete cosine transform is proposed and its orthogonality is proved. Finally, we propose a generalized discrete W transform with three parameters, and prove its orthogonality for some new cases. Keywords: Discrete Fourier transform, discrete sine transform, discrete cosine transform, discrete W transform Nigerian Journal of Technological Research , vol7(1) 2012

On discrete cosine transform

In this paper, we propose a simple, non iterative blocking artifacts reduction method for block discrete cosine transform (DCT) compressed images, using adaptive bilateral filter. Bilateral filter when applied on a input image, smooths out the blocking artifacts by weighted averaging of the pixel values without smoothing the edges. The proper selection of the bilateral filter parameters is very important and affects the filtering results significantly. We select the bilateral filter parameters optimally, through empirical study. The bilateral filter parameters are made adaptive to different decompressed images using different quantization tables. Our main contribution in this paper is to select the bilateral filter parameters optimally and adaptively through empirical study, in application to image deblocking. The proposed method shows highly encouraging results both objectively and subjectively, when compared to many state of the art image deblocking schemes including a recent method based on adaptive bilateral filter.

Blocking artifacts reduction using adaptive bilateral filtering

This paper describes a method which uses Lapped orthogonal transform (LOT) domain adaptive enhanced Lee filter for despeckling SAR images Since LOT is block transform, the transform coefficients are first remapped into octave type form and then the enhanced Lee filtering is applied to subband LOT coefficients The motivation of using Lapped transform lies in its ability to preserve oscillatory type of features present in the images like textures For more edge preservation during despeckling process, the modified ratio of averages (MROA) edge detector is applied to the approximation subband to obtain edge information which is then employed in the proposed framework to obtain edge information in other subbands Based on this edge information we classify the edge and non edge coefficients in all oriented subbands at various decomposition levels Experiments using true SAR images show that the LOT domain enhanced Lee filter in proposed edge preserving framework smoothes the speckle very well in homogeneous regions while preserving more edges and texture information The proposed despeckling filter shows significant improvement over enhanced Lee filtering in spatial and wavelet domain and also outperforms one recent undecimated wavelet domain method

A lapped transform domain enhanced lee filter with edge detection for speckle noise reduction in SAR images

In this paper, a new texture descriptor is proposed for face and biomedical image retrieval. The proposed texture descriptor extracts the co-occurrence of similar center-symmetric local binary edge...

An effective texture descriptor for retrieval of biomedical and face images based on co-occurrence of similar center-symmetric local binary edges

In this paper, a new non iterative method is proposed in the wavelet domain for the reduction of blocking artifacts in block discrete cosine transform (DCT) compressed images. The method employs a local Wiener filter in wavelet domain to smooth out the blocking artifacts. The local variance is used in the same domain for pixel adaptive processing. We have demonstrated that the noise standard deviation has a strong correlation with the average of first 3×3 values from the quantization matrix. The proposed method outperforms several well know image deblocking methods both objectively and subjectively.

Blocking artifacts suppression in Wavelet transform domain using local Wiener filtering

A new Lapped transform domain SAR image despeckling algorithm using a two-state Gaussian mixture probability density function that uses local parameters for the mixture model, is proposed. The use of lapped orthogonal transform (LOT) is motivated by its low computational complexity and robustness to oversmoothing. It is shown that the dyadic rearranged LOT coefficients of logarithmically transformed SAR images can be well approximated using two-state Gaussian mixture distribution compared to Laplacian, Gamma, generalized Gaussian and Cauchy distributions, based on the Kolmogorov–Smirnov (KS) goodness of fit test. The LOT coefficients of speckle noise are modeled using zero mean Gaussian distributions. A maximum a posteriori (MAP) estimator within Bayesian framework is developed using this proposed prior distribution and is used to restore the noisy LOT coefficients. The parameters of mixture distribution are estimated using the expectation-maximization algorithm. This paper presents a new technique to identify LOT modulus maxima which allows us to classify LOT coefficients into the edge and non edge coefficients. The classified edge coefficients are kept unmodified by the proposed algorithm whereas the noise-free estimates of non-edge coefficients are obtained using Bayesian MAP estimator developed using two state Gaussian mixture distribution with local parameters. Finally the proposed technique is combined with the cycle spinning scheme to further improve the despeckling performance. Experimental results show that the proposed method very efficiently reduces speckle in homogeneous regions while preserving more edge structures compared to some recent well known methods.

Deepika Hazarika

Papers

Blocking artifacts reduction using adaptive bilateral filtering

A lapped transform domain enhanced lee filter with edge detection for speckle noise reduction in SAR images

An effective texture descriptor for retrieval of biomedical and face images based on co-occurrence of similar center-symmetric local binary edges

Blocking artifacts suppression in Wavelet transform domain using local Wiener filtering

SAR Image Despeckling Based on a Mixture of Gaussian Distributions with Local Parameters and Multiscale Edge Detection in Lapped Transform Domain